Construction of a risk prediction model for detecting postintensive care syndrome-mental disorders.

BACKGROUND: Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem. AIMS: To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency. STUDY DESIGN: In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency. RESULTS: The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model. CONCLUSIONS: The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures. RELEVANCE TO CLINICAL PRACTICE: The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.

Development and validation of an intensive care unit acquired weakness prediction model: A cohort study.

Background: At present, intensive care unit acquired weakness (ICU-AW) has become an important health care issue. The aim of this study was to develop and validate an ICU-AW prediction model for adult patients in intensive care unit (ICU) to provide a practical tool for early clinical diagnosis. Methods: An observational cohort study was conducted including 400 adult patients admitted from September 2021 to June 2022 at an ICU with four ward at a medical university affiliated hospital in China. The Medical Research Council (MRC) scale was used to assess bedside muscle strength in ICU patients as a diagnostic basis for ICUAW. Patients were divided into the ICU-AW group and the no ICU-AW group and the clinical data of the two groups were statistically analyzed. A risk prediction model was then developed using binary logistic regression. Sensitivity, specificity, and the area under the curve (AUC) were used to evaluate the predictive ability of the model. The Hosmer-Lemeshow test was used to assess the model fit. The bootstrap method was used for internal verification of the model. In addition, the data of 120 patients in the validation group were selected for external validation of the model. Results: The prediction model contained five risk factors: gender (OR: 4.31, 95% CI: 1.682-11.042), shock (OR: 3.473, 95% CI: 1.191-10.122), mechanical ventilation time (OR: 1.592, 95% CI: 1.317-1.925), length of ICU stay (OR: 1.085, 95% CI: 1.018-1.156) and age (OR: 1.075, 95% CI: 1.036-1.115). The AUC of this model was 0.904 (95% CI: 0.847-0.961), with sensitivity of 87.5%, specificity of 85.8%, and Youden index of 0.733. The AUC of the model after resampling is 0.889. The model verification results showed that the sensitivity, specificity and accuracy were 71.4, 92.9, and 92.9%, respectively. Conclusion: An accurate, and readily implementable, risk prediction model for ICU-AW has been developed. This model uses readily obtained variables to predict patient ICU-AW risk. This model provides a tool for early clinical screening for ICU-AW.

A systematic review and meta-analysis of risk factors for intensive care unit acquired weakness.

BACKGROUND: The aim of this meta-analysis is to systematically evaluate and summarize the risk factors of intensive care unit acquired weakness (ICU-AW), to provide evidence-based evidence for the formulation of prevention strategies for ICU-AW. METHODS: PubMed, EMBASE, Web of Science, CBM (China Biology Medicine, China), Chinese National Knowledge Infrastructure, Chinese WANFANG, and VIP will be searched to define relevant risk factors for ICU-AW. The databases search period is from January 1, 2005 to August 13, 2021. The Newcastle Ottawa Scale (NOS) is used to evaluate the quality of the included studies. RevMan 5.3 analysis software will be used for meta-analysis. RESULTS: This systematic review and meta-analysis included a total of 12 cohort studies, including 9 international journals and 3 Chinese journals, with a total of 1950 patients, of which 856 had ICU-AW. The results showed that the significant risk factors for ICU-AW included female (odds ratio [OR] = 1.34, 95% confidence interval [CI]: 1.06-1.71; P = .02), mechanical ventilation days (OR = 3.04, 95% CI: 1.82-4.26; P < .00001), age (OR = 6.33, 95% CI: 5.05-7.61; P < .00001), length of intensive care unit (ICU) stay (OR = 3.78, 95% CI: 2.06-5.51; P < .0001), infectious disease (OR = 1.67, 95% CI: 1.20-2.33; P = .002), renal replacement therapy (OR = 1.59, 95% CI: 1.11-2.28; P = .01), use of aminoglucoside drugs (OR = 2.51, 95% CI: 1.54-4.08; P = .0002), sepsis related organ failure assessment (SOFA) score (OR = 1.07, 95% CI: 0.24-1.90; P = .01), hyperglycemia (OR = 2.95, 95% CI: 1.70-5.11; P = .0001). CONCLUSION: This meta-analysis provides comprehensive evidence-based on the assessment of the risk factors for ICU-AW, their multifactorial etiology was confirmed. This study indicated that female, mechanical ventilation days, age, length of ICU stay, infectious disease, renal replacement therapy, use of aminoglucoside drugs, SOFA score, and hyperglycemia are independent risk factors for ICU-AW. We have not found consistent evidence that corticosteroids, neuromuscular blockers, sepsis have any effect on ICU-AW risk.